143258 research outputs found
Sort by
Effectiveness and producers perceptions of camera-based technology detecting hoof lesions In dairy cows
Get PDFUniversity of Minnesota Ph.D. dissertation. July 2025. Major: Veterinary Medicine. Advisor: Gerard Cramer. 1 computer file (PDF); iii, 349 pages.Lameness remains a significant concern in the dairy industry, with growing interest in automated technologies for early detection and intervention. This dissertation combines quantitative, qualitative, and machine learning approaches to assess both the technical performance of an autonomous camera system and stakeholder perspectives on technology implementation. The first objective of this thesis was to evaluate how previous research has applied machine learning methods for detecting lameness and hoof lesions in dairy cattle (Chapter 1). The second and third objectives assessed the performance of an automated camera-based system that scores locomotion and body condition. Chapter 2 examined whether the system's locomotion scores were associated with hoof lesion outcomes, using hoof trimming data to compare cows with and without lesions. Chapter 3 evaluated the system’s ability to reliably identify individual cows. Chapter 4 assessed inter- and intra-observer reliability across different methods of body condition scoring, including human observers, photo-based scoring, and the automated system. Recognizing the role of human perspectives in technology adoption, Chapters 5 and 6 explored perceptions of lameness and lameness detection technologies among dairy farm decision-makers. These chapters focused on stakeholders view lameness management priorities and barriers to adopting automated technologies. Finally, Chapter 7 evaluated existing locomotion score-based thresholds for identifying cows with hoof lesions, while Chapter 8 developed a machine learning algorithm to improve classification of cows requiring intervention for hoof lesions. Together, these chapters contribute to advancing both the technical capabilities and real-world applicability of autonomous lameness detection technologies in the dairy industry.Swartz, Drew. (2025). Effectiveness and producers perceptions of camera-based technology detecting hoof lesions In dairy cows. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/277400
Labovitz (2025 Fall)
Get PDFUniversity of Minnesota Duluth. Labovitz School of Business and Economics. (2025). Labovitz (2025 Fall). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/277148
University of Minnesota Presidential AI Task Force Report AI in Education Work Group
Get PDFThe rapid evolution of Artificial Intelligence (AI) is significantly changing the landscape of higher education, with the potential to impact all University functions. In response to the growing interest and needs expressed by the University community, the AI Task Force was charged with developing recommendations for policies and investments concerning the use of AI in education, research, and operations. This report specifically addresses the relationship between AI in education.University of Minnesota. Presidential AI Task Force. AI in Education Work Group. (2025). University of Minnesota Presidential AI Task Force Report AI in Education Work Group. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276898
Episode 307 - What is Causing All of Our Cow Abortions - UMN Extension's The Moos Room
Get PDFRuntime 20:10Brad records solo during a busy fair season. But behind the scenes at the research center, a troubling issue emerged this summer: a cluster of abortions isolated to a 50–60 cow organic herd.After ruling out other groups, Brad suspected moldy feed. Testing revealed high mold counts—especially Fusarium, which produces mycotoxins linked to infertility and abortions. The herd had been eating first-crop hay baled a bit too wet, later found to be heating and moldy.Aborted fetuses sent to diagnostic labs showed mixed results: one indicated Neospora caninum (a protozoan parasite spread by dogs or coyotes), another pointed to bacterial placentitis likely linked to moldy feed. Despite the confusion, abortions dropped sharply after the moldy hay was removed from the diet, strengthening Brad’s belief that feed quality was the main culprit.To prevent future issues, the team pulled suspect hay from use, began feeding a mycotoxin binder, and emphasized the importance—and challenge—of making mold-free feed in a wet year.After almost a month without new cases in the affected herd, Brad is cautiously optimistic. His takeaway: good feed management is critical, even for research herds, and sometimes the simplest solution—removing bad feed—makes the biggest difference.Heins, Brad; Krekelberg, Emily. (2025). Episode 307 - What is Causing All of Our Cow Abortions - UMN Extension's The Moos Room. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276587
The immunobiology of cardiac macrophages: multifaceted drivers of dysfunction and resolution across the spectrum of heart failure
Get PDFUniversity of Minnesota Ph.D. dissertation. June 2025. Major: Integrative Biology and Physiology. Advisor: Xavier Revelo. 1 computer file (PDF); xi, 177 pages.Cardiovascular diseases remain the leading cause of death worldwide. Recent studies have revealed critical roles for inflammation in the onset and progression of heart disease, with macrophages emerging as key immune players in this context. Cardiac macrophages - highly plastic cells with diverse phenotypes - originate from two major sources thatinfluence their functions. Cardiac resident macrophages (CRMs) arise from embryonic progenitors and populate the heart during development, while monocyte-derived macrophages (MoMFs) are recruited from the bone marrow in response to injury. MoMFs typically mediate inflammatory responses and contribute to tissue damage, whereas CRMs are generally anti-inflammatory and play protective roles in both homeostasis and disease. However, CRMs are a heterogeneous population comprising multiple subtypes with distinct genetic signatures that influence their function across various disease states.
This thesis investigates how macrophage heterogeneity, arising both from their origin and microenvironment, relates to their functional roles. Using both acute and chronic injury models, I examine the diverse roles of cardiac macrophages in modulating disease outcomes, with a specific focus on their bidirectional influence. In a model of acute pressure overload, I define CRM contributions to both fibrosis and angiogenesis. While overall a protective cell population, I identify a subset of CRMs that can aggravate fibrosis through the release of Chemokine (C-C motif) ligand 24 (CCL24). Finally, I characterize
a specialized subset of cardiac macrophages known as lipid-associated macrophages (LAMs), which exhibit conserved genetic features across tissues and play a role in modulating disease in obesity- and hypertension-associated heart failure.
Together, these studies highlight the functional duality and context-specific behavior of macrophages in cardiac pathology. This thesis underscores the importance of dissecting macrophage heterogeneity to inform targeted therapeutic strategies for heart failure.Parthiban, Preethy. (2025). The immunobiology of cardiac macrophages: multifaceted drivers of dysfunction and resolution across the spectrum of heart failure. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276806
Episode 308 - Battling BLV – Updates on Bovine Leukosis Virus in the UMN Herd - UMN Extension's The Moos Room
Get PDFRuntime 19:26In this episode of The Moos Room, Brad shares updates on the University of Minnesota’s ongoing work with bovine leukosis virus (BLV), a retrovirus that weakens the immune system, reduces production, and costs dairy farmers hundreds of dollars per cow each year.Brad walks through the latest herd testing results, where prevalence has held steady at around 30%, but with new infections continuing to appear—especially in older cows. He digs into the role of biting flies in BLV transmission, highlighting research showing that nearly all previously negative cows became suspect or positive after just one summer on pasture.The discussion covers:How BLV spreads within herds.The economic and animal health impacts of infection.Management strategies like testing, culling, colostrum protocols, and breeding decisions.Why fly control may be one of the most important tools for reducing BLV spread in grazing herds.Tune in to hear how the UMN Morris dairy herd is tackling this challenge, what the research says about seasonality and transmission, and what steps farmers can take to manage BLV on their own operations.Heins, Brad; Krekelberg, Emily. (2025). Episode 308 - Battling BLV – Updates on Bovine Leukosis Virus in the UMN Herd - UMN Extension's The Moos Room. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276586
Methodological advances in structured statistical learning
Get PDFUniversity of Minnesota Ph.D. dissertation. June 2025. Major: Statistics. Advisor: Adam Rothman. 1 computer file (PDF); vii, 146 pages.This dissertation explores methodological advances in statistical learning, specifically addressing two fundamental challenges: the interpretability of complex association structures in multivariate categorical data analysis, and privacy-preserving distributed inference across heterogeneous datasets. First, we propose a penalized likelihood framework tailored for multivariate categorical response regression, encompassing classical discrete graphical models such as the Ising model, Potts model, and hypergraph models. Utilizing a distinctive subspace decomposition, the method explicitly captures mutual, joint, and conditionally independent associations between categorical variables, facilitating interpretable representations of association structures. We derive theoretical guarantees, establishing error bounds that hold particularly in high-dimensional contexts. Comprehensive simulation studies demonstrate that our approach achieves greater interpretability and improved predictive accuracy compared to existing methods. Second, we tackle data-sharing challenges under stringent privacy constraints and site heterogeneity, common in multi-site clinical trials. We introduce a robust distributed algorithm for high-dimensional inference and structure learning. Our heterogeneous model integrates global and site-specific effects, employing nonconvex regularization via difference of convex programming under an l0 constraint, ensuring selection consistency and computational feasibility. Despite the underlying optimization being NP-hard, our method converges globally in polynomial time under realistic conditions. By exclusively penalizing nuisance parameters, our approach maintains valid statistical inference, directly addressing practical data-sharing constraints.Zhao, Hongru. (2025). Methodological advances in structured statistical learning. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276734
Minutes: P&A Consultative Committee: October 16, 2025
Get PDFUniversity of Minnesota: P&A Consultative Committee. (2025). Minutes: P&A Consultative Committee: October 16, 2025. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/277089
A participatory science approach to monitoring and predicting within-lake zebra mussel abundance
Get PDFUniversity of Minnesota M.S. thesis.June 2025. Major: Conservation Sciences. Advisors: Nicholas Phelps, Alex Bajcz. 1 computer file (PDF); iv, 72 pages.Zebra mussels (Dreissena polymorpha) are an invasive species that disrupt ecosystems and increase management costs across North America. To monitor their spread and support data-driven management, we developed the Zebra Mussel Safari—a scalable participatory science program. After two pilot years, we engaged 63 participants from 7 Minnesota lakes in 2023 and expanded to 154 participants from 15 lakes in 2024. Volunteers deployed samplers from their docks to measure juvenile settlement, photographed the samplers, and submitted the images online. Automated counts were accurate and efficient, reducing analysis time compared to manual methods. Participant feedback was positive, indicating strong potential for long-term engagement and high-quality data collection at scale. Using data from the program, we explored four Negative Binomial generalized linear models (GLM), varying in sample size, predictor diversity, and cross-validation strategy. Each model search identified a set of high-performing models based on both in-sample fit and out-of-sample predictive performance. Key predictors consistently retained across top models included Secchi depth, number of boat accesses, years since first known infestation, fetch, groundwater calcium, and a lake level suitability score. Across all model searches, predictions were accurate in identifying sites with low or high zebra mussel abundances; however, moderate abundances proved more challenging with consistent overprediction. This work demonstrates the importance of continued monitoring and the potential of public data collection to inform data-driven invasive species management strategies.Lorentz, Sawyer. (2025). A participatory science approach to monitoring and predicting within-lake zebra mussel abundance. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/276714
Episode 10 - Search, apply, repeat, Library Table Talk
No full text64:57 minutes runtime. For more information, please visit the Library Table Talk website at z.umn.edu/LibraryTableTalk.In which we talk with Michelle Colquitt, Jasmine Kirby, Kolby Peixoto, and Joan Petit about that most harrowing of processes, job searching. They bring perspectives from both the job candidate side and hiring committee side to share advice and insight, and offer commiseration and encouragement for the job seekers out there.Cabullo, Hannah; Sparrow, Stephanie. (2025). Episode 10 - Search, apply, repeat, Library Table Talk. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/277269